Difference between revisions of "The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment"

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{{AcademicPaper
 
{{AcademicPaper
|Title=Hubs (Academic Paper)
+
|Has title=The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment
|Author=Ed Egan, Yael Hochberg
+
|Has author=Ed Egan, Yael Hochberg
|RAs=Ariel Sun
+
|Has RAs=Hira Farooqi,
|Status=In development
+
|Has paper status=Tabled
 
}}
 
}}
 
 
 
=Hubs Pages=
 
=Hubs Pages=
*This page [[Hubs (Academic Paper)]] is the main page for the Hubs project!
+
*This page [[Hubs (Academic Paper)]] contains only the abstract and some useful refs
*The old work done by Rachael is on the [[Hubs]] page
+
*The main [[Hubs]] page is the place to go!
 +
*There is also [[Old Completed Work on Hubs]]
 
*For a high-level overview of the variables for the scorecard go to [[Hubs Scorecard (Academic Paper)]]. This summarizes:
 
*For a high-level overview of the variables for the scorecard go to [[Hubs Scorecard (Academic Paper)]]. This summarizes:
 
**Current work in progress for building the Hubs scorecard: [[Hubs: Hubs Scorecard]]
 
**Current work in progress for building the Hubs scorecard: [[Hubs: Hubs Scorecard]]
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=Abstract=
+
==Abstract==
 
 
The Hubs Research Project is a full-length academic paper analyzing the effectiveness of "hubs", a component of the entrepreneurship ecosystem, in the advancement and growth of entrepreneurial success in a metropolitan area.
 
  
This research will primarily be focused on large and mid-sized Metropolitan Statistical Areas (MSAs), as that is where the greater majority of Venture Capital funding is located.
+
Entrepreneurship hubs have recently emerged as a stable institutional form and as popular and important components of entrepreneurship ecosystems. Hubs are membership-based co-working flex-spaces with specialized services and resources for nascent start-up firms. Examples of hubs include the Capital Factory in Austin, Texas, 1871 in Chicago, Illinois, and 1776 in Philadelphia, Pennsylvania. Each of these hubs has around 50,000sqft of workspace for almost a thousand members working at hundreds of start-ups. Each also includes an accelerator program, has daily events, classes and meetings related to entrepreneurship, and hosts venture capitalists, angel investors, and service firms.
 +
Hubs provide a very high degree of agglomeration. Agglomeration is particularly important in entrepreneurship because it facilitates learning and failure is frequent. Entrepreneurs can then learn from other entrepreneurs as well as industry professionals; and when a start-up based in a hub fails, the firm’s human resources can be quickly and efficiently absorbed into another venture. We might therefore expect that the introduction of a hub will lead to a greater degree of entrepreneurial activity in a region.
 +
This paper will use a difference-in-difference approach to estimate the effect of the introduction of a hub on seed and early stage venture capital investment in an area. The empirical methodology of the paper is closely aligned with the methodology in Fedher and Hochberg (2015). The decision of a hub to locate itself in an area is expected to be highly correlated with existing characteristics of the area, unobserved in the data, which induces a significant endogeneity bias in the model. To rectify this issue the methodology proceeds in two steps. In the first step, a hazard model is estimated which predicts the probability that a hub will come to an area. In the second stage these predicted probabilities are used to find a match for each treated region by finding the untreated region with the most similar probability of founding an accelerator in that year when the treated region is on the common support.
  
A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].
+
==Current Work==
  
 +
===General Overview===
  
=Current Work=
 
==General Overview==
 
 
Currently there are '''3''' major tasks being performed (list to be updated):
 
Currently there are '''3''' major tasks being performed (list to be updated):
 
#'''Creation of VC data table''': '''UPDATE: Complete''' (see completed work section below)
 
#'''Creation of VC data table''': '''UPDATE: Complete''' (see completed work section below)
Line 31: Line 29:
 
#'''Hazard Rate Model''': '''UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates.''' In order to perform our diff-diff model, we need to match MSAs.  In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.
 
#'''Hazard Rate Model''': '''UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates.''' In order to perform our diff-diff model, we need to match MSAs.  In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.
  
==Work In Progress==
 
 
 
=Completed Work=
 
==Venture Capital Data General Overview==
 
The main goal of the data set is to aggregate company, fund, and round level data to be analyzed at a combined MSA and year level. The data set is compromised of two major parts: a granular company/fund/round and an aggregated CMSA-Year.  The data includes all United States Venture Capital transactions (moneytree) from the twenty-five year period of 1990 through 2015.
 
 
The Hubs data set, from SDC Platinum, has been constructed in the server:
 
Data files are in 128.42.44.181/bulk/Hubs
 
All files are in 128.42.44.182/bulk/Projects/Hubs
 
psql Hubs2
 
 
Sql files:
 
:<code>E:\McNair\Projects\Hubs\Data Script v10.txt</code>
 
Note: We need to check that everything in '''Data Script v9 Ariel.txt''' has been incorporated into v10
 
 
Table Header Rows + 5 lines:
 
:<code>E:\McNair\Projects\Hubs\Data Table List v2.txt</code>
 
Note: This was generated by '''Data Script v10.txt'''
 
 
===Procedure - Granular Table===
 
#Start with separate raw datasets for Companies, Funds, and Rounds - '''Locate Raw Datasets and Determine Pedigree'''
 
#Add Data to Each Individual dataset (e.g. add MSA code)
 
#Clean and standardize names (e.g. company or fund name) for each dataset
 
#Join the Datasets (here we need to exclude undisclosed companies)
 
 
===Procedure - CMSA-Year Table===
 
#Create a consistent CMSA-Year table to be used later
 
#Using the tables from the granular table, parse out the right data
 
#Join the parsed out data with the CMSA-Year Table
 
#Join these Tables
 
 
==VC Specific Tables and Procedure==
 
===Raw data tables===
 
#'''Funds''': fund name, first investment date, last investment date, fund closing date, address, known investment, average investment, number of companies invested, MSA, MSA code.
 
#'''Rounds''': round date, company name, state, round number, stage 1, stage 2, stage 3
 
#'''Combined Rounds''': company name, round date, disclosed amount, investor
 
#'''Companies''': company name, first investment, last investment, MSA, MSA code, address, state, date founded, known funding, industry
 
#'''MSA List''': MSA, MSA code, CMSA, CMSA code
 
#'''Industry List''': changes 6 industry categories to 4— ICT, Life Sciences, Semiconductors, Other
 
 
 
===Granular Table (Fund-Round-Company)===
 
The final table here contains all venture capital transactions by disclosed funds and portfolio companies, together with their CMSAs.
 
To get the table, we processed the raw data sets in the following steps:
 
#Clean '''Company''' data
 
##Import raw data companies
 
##Add variable 'CMSA' from data set MSA list, update variable 'industry' by joining data set industry list
 
##Remove duplicates and remove undisclosed companies
 
#Clean '''Fund''' data
 
##Import raw data funds
 
##Add variable 'CMSA'
 
##Remove duplicates and remove undisclosed funds
 
##Match fund names with itself using [[The Matcher (Tool) |The Matcher]] to get the standard fund names
 
#Clean '''Round''' data
 
##Import raw data rounds and combined rounds
 
##Add variables 'number of investment', 'estimated investment' and 'year'
 
##Remove duplicates and remove undisclosed funds
 
#'''Combine''' '''Companies''' and '''Rounds'''
 
##Combine cleaned companies and rounds data table on company names
 
##Add variable 'round number' and 'stage'
 
##Remove duplicates
 
#'''Combine''' '''Funds''' and '''rounds-companies'''
 
##Match fund names in rounds data table with standard fund names using [[The Matcher (Tool) |The Matcher]] to standardize fund names in rounds data table
 
##Join standard fund names to rounds-companies table
 
##Join cleaned funds table to rounds-companies table on standard fund names
 
 
Note: This was done by Ariel and then edited by Todd.
 
 
===CMSA-Year Aggregated Table===
 
 
The original MSA to CMSA was done by Rachel and used here. '''LOCATE THE FILE!!!'''
 
 
The final table contains number of companies and amount of investment, categorized by distance and stages, of each CMSA.
 
 
We processed data as follows:
 
#Create the '''CMSA-Year''' Table
 
##Create single variable tables: Distinct CMSA, year, stage, found year of fund and found year of company.
 
##Create the cross production tables: CMSA-year, CMSA-year-fund year founded and CMSA-year-company year founded
 
#Draw data from cleaned companies, funds and rounds tables
 
##Create a table with 'CMSA', 'number of companies' and 'year Founded' from cleaned companies table and join it to CMSA -year founded
 
##Create a table with 'Company CMSA', 'round year', 'disclosed amount' from rounds-companies combined table, and add stage binary variables. Join it to CMSA-year-company year founded
 
##Create a table with 'CMSA', 'fund year', 'number of investors' from cleaned funds table and join it to CMSA-year-fund year founded
 
#Create '''near-far''' and stages table
 
##Add fund data to rounds-companies
 
##Create near-far and stages binary variable
 
##Count investment and deals by CMSA and year, categorized by near-far and stages
 
#Combine all tables by CMSA and round-year
 
 
==Supplementary Data Sets==
 
 
Supplementary data sets are cleaned and joined back to CMSAyear table on CMSA and year:
 
 
#Number of STEM graduate student, by university and year(2005 to 2014).
 
#University R&D spending, by university and year(2004 to 2014).
 
#Income per capital, by MSA and year(2000 to 2012)
 
#Wages and salaries, by MSA and year(2000 to 2012)
 
 
All of these files were created originally by Rachel. Some were cleaned in Excel. No new data was added (some extra cols, no extra rows).
 
 
The datasets can respectively be found at:
 
E:\McNair\Projects\Hubs\STEM grads for upload v2.xls
 
  --Contains: university zipcode newmsacode msa msacode cmsa cmsacode year nostudents
 
  --CMSA code inside sheet seems to be ours. Check with Ariel.
 
E:\McNair\Projects\Hubs\NSF spending for upload.xls
 
  --Contains: Institution MSA CMSA code Year Spending
 
  --We think the CMSA Code is ours. Check with Ariel.
 
E:\McNair\Projects\Hubs\Income per capita upload.xls
 
  --Contains: Fips Area Year Income
 
  --Lookup to CMSA was done using VLOOKUPs in Excel. See Matcher Helper vTR.xls, and other Matcher Helper ???.xls files
 
E:\McNair\Projects\Hubs\Wage for upload v2.xls
 
  --Contains: Fips MSA Year Wage
 
  --Lookup to CMSA was done using VLOOKUPs in Excel. See Matcher Helper vTR.xls, and other Matcher Helper ???.xls files
 
  
=Resources=
+
==Resources==
  
 
===Additional Resources===
 
===Additional Resources===
 +
* A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].
 
* Yael Hochberg and Fehder (2015), located in dropbox
 
* Yael Hochberg and Fehder (2015), located in dropbox
 
** Use this paper as a guideline on how to conduct the analysis
 
** Use this paper as a guideline on how to conduct the analysis

Latest revision as of 10:56, 18 March 2019

Academic Paper
Title The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment
Author Ed Egan, Yael Hochberg
RAs Hira Farooqi
Status Tabled
© edegan.com, 2016

Hubs Pages


Abstract

Entrepreneurship hubs have recently emerged as a stable institutional form and as popular and important components of entrepreneurship ecosystems. Hubs are membership-based co-working flex-spaces with specialized services and resources for nascent start-up firms. Examples of hubs include the Capital Factory in Austin, Texas, 1871 in Chicago, Illinois, and 1776 in Philadelphia, Pennsylvania. Each of these hubs has around 50,000sqft of workspace for almost a thousand members working at hundreds of start-ups. Each also includes an accelerator program, has daily events, classes and meetings related to entrepreneurship, and hosts venture capitalists, angel investors, and service firms. Hubs provide a very high degree of agglomeration. Agglomeration is particularly important in entrepreneurship because it facilitates learning and failure is frequent. Entrepreneurs can then learn from other entrepreneurs as well as industry professionals; and when a start-up based in a hub fails, the firm’s human resources can be quickly and efficiently absorbed into another venture. We might therefore expect that the introduction of a hub will lead to a greater degree of entrepreneurial activity in a region. This paper will use a difference-in-difference approach to estimate the effect of the introduction of a hub on seed and early stage venture capital investment in an area. The empirical methodology of the paper is closely aligned with the methodology in Fedher and Hochberg (2015). The decision of a hub to locate itself in an area is expected to be highly correlated with existing characteristics of the area, unobserved in the data, which induces a significant endogeneity bias in the model. To rectify this issue the methodology proceeds in two steps. In the first step, a hazard model is estimated which predicts the probability that a hub will come to an area. In the second stage these predicted probabilities are used to find a match for each treated region by finding the untreated region with the most similar probability of founding an accelerator in that year when the treated region is on the common support.

Current Work

General Overview

Currently there are 3 major tasks being performed (list to be updated):

  1. Creation of VC data table: UPDATE: Complete (see completed work section below)
  2. Creation of Hubs Dataset: UPDATE: See current work in progress for updates We will collect key variables for potential Hubs.
  3. Hazard Rate Model: UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates. In order to perform our diff-diff model, we need to match MSAs. In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.


Resources

Additional Resources